Logistic Regression Under Sparse Data Conditions

• In Event: Adaptations of GLM for Advanced Data Analysis

Abstract

The impact of sparse data conditions among predictor variables on the estimation of binary logistic regression parameters is examined. Data were simulated under varying levels of data sparseness, sample size, and number of predictors. Parameter estimation bias and coverage probabilities are computed, and implications for data analysis discussed.

Authors

• Thomas J. Smith, Northern Illinois University

• David A. Walker, Northern Illinois University

• Cornelius McKenna, Kishwaukee College